The ability to index, query, and analyze large amounts of data from real-time sources remains a major headache for anyone involved in building database systems.
Data retrieval takes time and even that assumes that the nature of the queries lie within certain parameters. Overcoming the issue is possible but can be resource-intensive and involve complex engineering.
Meanwhile, the use cases for real-time data continue to multiply, not least in artificial intelligence (AI) applications connected to cybersecurity automation, fraud detection in financial services, and business analytics in sectors such as manufacturing.
These applications increasingly assume the ability to understand what is happening in data based on a view of the world as it is in the present, not as it was hours or days ago.
